An Interactive Computer Vision System for Tree Ring Analysis

Subah, Shahjadi Silma; Derminder, S.; Sanjeev, C.

doi:10.18520/cs/v112/i06/1262-1265

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers use MATLAB to implement their research. 1,7,11,12,13,14,15 However, the proposed algorithm is implemented by converting it to a program in Octave language as it's community edition is open source and its scripts are compatible with MATLAB scripts.…”

Section: Materials and Methodology Materialsmentioning

confidence: 99%

See 1 more Smart Citation

Copy Move Image Forgery Detection with Exact Match Block Based Technique

Arora

Singh

2019

Orient. J. Comp. Sci. and Technol

View full text Add to dashboard Cite

Digital images are a momentous part of today’s digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by matching the small blocks of size b b. The program is tested for 45 images of mixed image file formats by considering block sizes 2, 4, 6, 8, 10, 12, 14, and 16. It is observed from the experimental results that the proposed algorithm can detect copy-move image forgery in TIF, BMP and PNG image formats only. Results reveal that as the block size increases, execution time (time taken by CPU to display output) also increases but the number of detected forged images increases till block size 10 and attains saturation thereafter. Consequently block size should be set to 10 for getting good results in terms of less execution time.

show abstract

Section: Materials and Methodology Materialsmentioning

confidence: 99%

“…A standard formula is used to convert RGB image into grayscale image which is I = 0.299 R +0.587G + 0.114B where R, G, B are the three frames a coloured input image and I is the resulting image. 4,11,15…”

Section: Pre-processingmentioning

confidence: 99%

Copy Move Image Forgery Detection with Exact Match Block Based Technique

Arora

Singh

2019

Orient. J. Comp. Sci. and Technol

View full text Add to dashboard Cite

show abstract

“…To further automate the processing of digital analysis of wood samples, low hanging fruit will be the automatic detection of visual properties. Initial algorithms correctly identified almost 100% of rings in four conifer species, roughly 85% in six diffuse‐porous species, but only 40%–50% in two ring‐porous species (Fabijańska et al., 2017; Subah et al., 2017), despite being based on comparatively small and homogeneous training data. Recently, convolutional neural networks have also succeeded at segmenting anatomical sections of wood (Garcia‐Pedrero et al., 2018).…”

Section: Anticipated Developments and Future Potentialmentioning

confidence: 99%

The Wood Image Analysis and Dataset (WIAD): Open‐access visual analysis tools to advance the ecological data revolution

et al. 2021

View full text Add to dashboard Cite

1. Ecological data are collected and shared at an increasingly rapid pace, but it is often shared in inconsistent and untraceable processed forms. Images of wood contain a wealth of information such as colours and textures but are most commonly reduced to ring-width measurements before they can be shared in various common file formats. Archiving digital images of wood samples in libraries, which have been developed for ecological analysis and are publicly available, remains the exception.2. We developed the Wood Image Analysis and Dataset (WIAD), an open-source application including a web interface to integrate basic visual analysis of wood samples, such as increment cores, thin sections or X-ray films, basic data processing, and archiving of the images and derived data to facilitate transparency and reproducibility in studies using visual characteristics of wood.3. WIAD provides user-friendly tools to manipulate images of wood samples, mark and measure wood characteristics such as growth increments, density fluctuations, early-and latewood widths and fire scars, and to visualise, process and archive images, metadata, and the derived data. 4. WIAD constitutes a step towards the reproducible automation of tree-ring analysis while establishing an open-source foundation to create improved communitydeveloped repositories which would enable novel ecological studies harnessing the wealth of existing visual data.

show abstract

“…Nevertheless, from a technical perspective, it is crucial that both the quality of the acquired images and the capacity of the interpretation software provide sufficient information regarding tree rings of the logs (Subah et al 2017;Cruz-García et al 2019). The sensors installed on the processor head and operating in the forest on unprepared surfaces (the chain saw cutting surface is relatively rough for this use) may be incapable of returning the required quality for a deep and reliable analysis.…”

Section: Tree-ring Structurementioning

confidence: 99%

Smart Harvest Operations and Timber Processing for Improved Forest Management

Picchi

Sandak²,

Grigolato

et al. 2021

Managing Forest Ecosystems

View full text Add to dashboard Cite

Climate-smart forestry can be regarded as the evolution of traditional silviculture. As such, it must rely on smart harvesting equipment and techniques for a reliable and effective application. The introduction of sensors and digital information technologies in forest inventories, operation planning, and work execution enables the achievement of the desired results and provides a range of additional opportunities and data. The latter may help to better understand the results of management options on forest health, timber quality, and many other applications. The introduction of intelligent forest machines may multiply the beneficial effect of digital data gathered for forest monitoring and management, resulting in forest harvesting operations being more sustainable in terms of costs and environment. The interaction can be pushed even further by including the timber processing industry, which assesses physical and chemical characteristics of wood with sensors to optimize the transformation process. With the support of an item-level traceability system, the same data could provide a formidable contribution to CSF. The “memory” of wood could support scientists to understand the response of trees to climate-induced stresses and to design accordingly an adaptive silviculture, contributing to forest resilience in the face of future changes due to human-induced climate alteration.

show abstract

An Interactive Computer Vision System for Tree Ring Analysis

Cited by 6 publications

References 7 publications

Copy Move Image Forgery Detection with Exact Match Block Based Technique

Copy Move Image Forgery Detection with Exact Match Block Based Technique

The Wood Image Analysis and Dataset (WIAD): Open‐access visual analysis tools to advance the ecological data revolution

Smart Harvest Operations and Timber Processing for Improved Forest Management

Contact Info

Product

Resources

About